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Mycobacterium avium subsp. paratuberculosis (MAP) causes Johne's disease or paratuberculosis (PTB), with important animal health and economic implications. There are no therapeutic strategies to control this disease, and vaccination with inactivated vaccines is limited in many countries because it can interfere with the intradermal test used for bovine tuberculosis detection. Thus, infected animals either get culled after a positive ELISA or fecal PCR result or die due to clinical disease. In this study, we review recent studies aimed to discover genetic markers which could help to identify and select cattle less susceptible and more resilient to PTB. In recent years, the genotyping and subsequent imputation to whole-genome sequence (WGS) has allowed the identification of single-nucleotide polymorphisms (SNPs), quantitative trait loci (QTL), and candidate genes in the Bos taurus genome associated with susceptibility to MAP infection. In most of these genome-wide association studies (GWAS), phenotypes were based on ante-mortem test results including serum ELISA, milk ELISA, and detection of MAP by fecal PCR and bacteriological culture. Cattle infected with MAP display lesions with distinct severity but the associations between host genetics and PTB-associated pathology had not been explored until very recently. On the contrary, the understanding of the mechanisms and genetic loci influencing pathogen resistance, and disease tolerance in asymptomatic individuals is currently very limited. The identification of long-time asymptomatic cattle that is able to resist the infection and/or tolerate the disease without having their health and milk production compromised is important for disease control and breeding purposes.

Businesses are increasingly aware of their responsibility to work with, and within, their communities towards a resilient and sustainable future for all. This is thanks, in part, to the Sendai Framework, which first recognized the critical role played in disaster risk reduction by the private sector as employers, innovators, producers, asset-holders and investors. ARISE, the UNISDR Private Sector Alliance for Disaster Resilient Societies, was closely engaged in the development of the Sendai Framework and now works to demonstrate the impact of disaster risk-informed decision-making by businesses, as well as the potential of businesses as willing partners in advancing resilience.

As edge computing environments become increasingly dynamic, the need for efficient job scheduling and proactive fault prevention is becoming paramount. In such environments, minimizing machine downtime and maintaining productivity are critical challenges. In this paper, we propose an integrated approach to scheduling optimization that combines deep learning-based fault prediction with Satisfiability Modulo Theories (SMT)-based scheduling techniques. The proposed system predicts fault probabilities for machines in real time by leveraging operational state features such as temperature, vibration, tool wear, and operating hours. These fault predictions are then used as inputs to the SMT solver, which dynamically optimizes job scheduling. The system ensures task completion within deadlines while minimizing fault risks and optimizing resource utilization. To achieve this, the deep learning model continuously updates fault probabilities through a rolling prediction mechanism, allowing the scheduling system to proactively adapt to changing machine conditions. The SMT solver incorporates these predictions into its optimization process, ensuring that the schedule dynamically reflects the latest system state. The proposed method has been evaluated in simulated production line scenarios, demonstrating significant reductions in machine faults, improved scheduling efficiency, and enhanced overall system reliability. By integrating predictive maintenance with optimization techniques, this research contributes to the development of robust and adaptive scheduling systems for dynamic production environments.

Abstract Deep learning-based intrusion detection systems (DL-IDS) have proven effective in detecting cyber threats. However, their vulnerability to adversarial attacks and environmental noise, particularly in industrial settings, limits practical application. Current IDS models often assume ideal conditions, overlooking noise and adversarial manipulations, leading to degraded performance when deployed in real-world environments. Additionally, the black-box nature of DL model complicates decision-making, especially in industrial control systems (ICS) network, where understanding model behavior is crucial. This paper introduces the eXplainable Cyber-Threat Detection Framework (XC-TDF), a novel solution designed to overcome these challenges. XC-TDF enhances robustness against noise and adversarial attacks using regularization and adversarial training respectively, and also improves transparency through an eXplainable Artificial Intelligence (XAI) module. Simulation results demonstrate its effectiveness, showing resilience to perturbation by achieving commendable accuracy of 100% and 99.4% on the Wustl-IIoT2021 and Edge-IIoT datasets, respectively.

The development of rolling element bearing fault diagnosis systems has attracted a great deal of attention due to bearing components having a high tendency toward unexpected failures. However, under low-speed operating conditions, the diagnosis of bearing components remains a problem. In this paper, the adaptive resilient stacked sparse autoencoder (ArSSAE) is proposed to compensate for the shortcomings of conventional fault diagnosis systems at low speed. The efficiency of the proposed ArSSAE model is initially assessed using the CWRU database. Then, the proposed model is evaluated on actual vibration analysis (VA) and acoustic emission (AE) signals measured on a bearing test rig at low operating speeds (48-480 rpm). Overall, the analysis demonstrates that the ArSSAE model is able to perform an accurate diagnosis of bearing components under low-speed conditions.